Numbers
Standard Numeric Types
A type tree for all subtypes of Number in Base is shown below. Abstract types have been marked, the rest are concrete types.
Number (Abstract Type)
├─ Complex
└─ Real (Abstract Type)
├─ AbstractFloat (Abstract Type)
│ ├─ Float16
│ ├─ Float32
│ ├─ Float64
│ └─ BigFloat
├─ Integer (Abstract Type)
│ ├─ Bool
│ ├─ Signed (Abstract Type)
│ │ ├─ Int8
│ │ ├─ Int16
│ │ ├─ Int32
│ │ ├─ Int64
│ │ ├─ Int128
│ │ └─ BigInt
│ └─ Unsigned (Abstract Type)
│ ├─ UInt8
│ ├─ UInt16
│ ├─ UInt32
│ ├─ UInt64
│ └─ UInt128
├─ Rational
└─ AbstractIrrational (Abstract Type)
└─ IrrationalAbstract number types
Core.Number — Type
Core.AbstractFloat — Type
Core.Signed — Type
Core.Unsigned — Type
Unsigned <: IntegerAbstract supertype for all unsigned integers.
Built-in unsigned integers are printed in hexadecimal, with prefix 0x, and can be entered in the same way.
Examples
julia> typemax(UInt8)
0xff
julia> Int(0x00d)
13
julia> unsigned(true)
0x0000000000000001sourceBase.AbstractIrrational — Type
AbstractIrrational <: RealNumber type representing an exact irrational value, which is automatically rounded to the correct precision in arithmetic operations with other numeric quantities.
Subtypes MyIrrational <: AbstractIrrational should implement at least ==(::MyIrrational, ::MyIrrational), hash(x::MyIrrational, h::UInt), and convert(::Type{F}, x::MyIrrational) where {F <: Union{BigFloat,Float32,Float64}}.
If a subtype is used to represent values that may occasionally be rational (e.g. a square-root type that represents √n for integers n will give a rational result when n is a perfect square), then it should also implement isinteger, iszero, isone, and == with Real values (since all of these default to false for AbstractIrrational types), as well as defining hash to equal that of the corresponding Rational.
Concrete number types
Core.Float16 — Type
Float16 <: AbstractFloat <: Real16-bit floating point number type (IEEE 754 standard). Binary format is 1 sign, 5 exponent, 10 fraction bits.
sourceCore.Float32 — Type
Float32 <: AbstractFloat <: Real32-bit floating point number type (IEEE 754 standard). Binary format is 1 sign, 8 exponent, 23 fraction bits.
The exponent for scientific notation should be entered as lower-case f, thus 2f3 === 2.0f0 * 10^3 === Float32(2_000). For array literals and comprehensions, the element type can be specified before the square brackets: Float32[1,4,9] == Float32[i^2 for i in 1:3].
See also Inf32, NaN32, Float16, exponent, frexp.
Core.Float64 — Type
Float64 <: AbstractFloat <: Real64-bit floating point number type (IEEE 754 standard). Binary format is 1 sign, 11 exponent, 52 fraction bits. See bitstring, signbit, exponent, frexp, and significand to access various bits.
This is the default for floating point literals, 1.0 isa Float64, and for many operations such as 1/2, 2pi, log(2), range(0,90,length=4). Unlike integers, this default does not change with Sys.WORD_SIZE.
The exponent for scientific notation can be entered as e or E, thus 2e3 === 2.0E3 === 2.0 * 10^3. Doing so is strongly preferred over 10^n because integers overflow, thus 2.0 * 10^19 < 0 but 2e19 > 0.
See also Inf, NaN, floatmax, Float32, Complex.
Base.MPFR.BigFloat — Type
Core.Bool — Type
Bool <: IntegerBoolean type, containing the values true and false.
Bool is a kind of number: false is numerically equal to 0 and true is numerically equal to 1. Moreover, false acts as a multiplicative "strong zero" against NaN and Inf:
julia> [true, false] == [1, 0]
true
julia> 42.0 + true
43.0
julia> 0 .* (NaN, Inf, -Inf)
(NaN, NaN, NaN)
julia> false .* (NaN, Inf, -Inf)
(0.0, 0.0, -0.0)Branches via if and other conditionals only accept Bool. There are no "truthy" values in Julia.
Comparisons typically return Bool, and broadcasted comparisons may return BitArray instead of an Array{Bool}.
julia> [1 2 3 4 5] .< pi
1×5 BitMatrix:
1 1 1 0 0
julia> map(>(pi), [1 2 3 4 5])
1×5 Matrix{Bool}:
0 0 0 1 1See also trues, falses, ifelse.
Core.UInt8 — Type
UInt8 <: Unsigned <: Integer8-bit unsigned integer type.
Printed in hexadecimal, thus 0x07 == 7.
sourceCore.Int16 — Type
Core.UInt16 — Type
UInt16 <: Unsigned <: Integer16-bit unsigned integer type.
Printed in hexadecimal, thus 0x000f == 15.
sourceCore.Int32 — Type
Core.UInt32 — Type
UInt32 <: Unsigned <: Integer32-bit unsigned integer type.
Printed in hexadecimal, thus 0x0000001f == 31.
sourceCore.Int64 — Type
Core.UInt64 — Type
UInt64 <: Unsigned <: Integer64-bit unsigned integer type.
Printed in hexadecimal, thus 0x000000000000003f == 63.
sourceCore.Int128 — Type
Core.UInt128 — Type
UInt128 <: Unsigned <: Integer128-bit unsigned integer type.
Printed in hexadecimal, thus 0x0000000000000000000000000000007f == 127.
sourceCore.Int — Type
IntSys.WORD_SIZE-bit signed integer type, Int <: Signed <: Integer <: Real.
This is the default type of most integer literals and is an alias for either Int32 or Int64, depending on Sys.WORD_SIZE. It is the type returned by functions such as length, and the standard type for indexing arrays.
Note that integers overflow without warning, thus typemax(Int) + 1 < 0 and 10^19 < 0. Overflow can be avoided by using BigInt. Very large integer literals will use a wider type, for instance 10_000_000_000_000_000_000 isa Int128.
Integer division is div alias ÷, whereas / acting on integers returns Float64.
See also Int64, widen, typemax, bitstring.
Base.GMP.BigInt — Type
Base.Complex — Type
Base.Rational — Type
Rational{T<:Integer} <: RealRational number type, with numerator and denominator of type T. Rationals are checked for overflow.
Base.Irrational — Type
Irrational{sym} <: AbstractIrrationalNumber type representing an exact irrational value denoted by the symbol sym, such as π, ℯ and γ.
See also AbstractIrrational.
Data Formats
Base.digits — Function
digits([T<:Integer], n::Integer; base::T = 10, pad::Integer = 1)Return an array with element type T (default Int) of the digits of n in the given base, optionally padded with zeros to a specified size. More significant digits are at higher indices, such that n == sum(digits[k]*base^(k-1) for k in 1:length(digits)).
See also ndigits, digits!, and for base 2 also bitstring, count_ones.
Examples
julia> digits(10)
2-element Vector{Int64}:
0
1
julia> digits(10, base = 2)
4-element Vector{Int64}:
0
1
0
1
julia> digits(-256, base = 10, pad = 5)
5-element Vector{Int64}:
-6
-5
-2
0
0
julia> n = rand(-999:999);
julia> n == evalpoly(13, digits(n, base = 13))
truesourceBase.digits! — Function
digits!(array, n::Integer; base::Integer = 10)Fills an array of the digits of n in the given base. More significant digits are at higher indices. If the array length is insufficient, the least significant digits are filled up to the array length. If the array length is excessive, the excess portion is filled with zeros.
Examples
julia> digits!([2, 2, 2, 2], 10, base = 2)
4-element Vector{Int64}:
0
1
0
1
julia> digits!([2, 2, 2, 2, 2, 2], 10, base = 2)
6-element Vector{Int64}:
0
1
0
1
0
0sourceBase.bitstring — Function
bitstring(n)A string giving the literal bit representation of a primitive type (in bigendian order, i.e. most-significant bit first).
See also count_ones, count_zeros, digits.
Examples
julia> bitstring(Int32(4))
"00000000000000000000000000000100"
julia> bitstring(2.2)
"0100000000000001100110011001100110011001100110011001100110011010"sourceBase.parse — Function
parse(::Type{SimpleColor}, rgb::String)An analogue of tryparse(SimpleColor, rgb::String) (which see), that raises an error instead of returning nothing.
parse(::Type{Platform}, triplet::AbstractString)Parses a string platform triplet back into a Platform object.
parse(type, str; base)Parse a string as a number. For Integer types, a base can be specified (the default is 10). For floating-point types, the string is parsed as a decimal floating-point number. Complex types are parsed from decimal strings of the form "R±Iim" as a Complex(R,I) of the requested type; "i" or "j" can also be used instead of "im", and "R" or "Iim" are also permitted. If the string does not contain a valid number, an error is raised.
Examples
julia> parse(Int, "1234")
1234
julia> parse(Int, "1234", base = 5)
194
julia> parse(Int, "afc", base = 16)
2812
julia> parse(Float64, "1.2e-3")
0.0012
julia> parse(Complex{Float64}, "3.2e-1 + 4.5im")
0.32 + 4.5imsourceBase.tryparse — Function
tryparse(::Type{SimpleColor}, rgb::String)Attempt to parse rgb as a SimpleColor. If rgb starts with # and has a length of 7, it is converted into a RGBTuple-backed SimpleColor. If rgb starts with a-z, rgb is interpreted as a color name and converted to a Symbol-backed SimpleColor.
Otherwise, nothing is returned.
Examples
julia> tryparse(SimpleColor, "blue")
SimpleColor(blue)
julia> tryparse(SimpleColor, "#9558b2")
SimpleColor(#9558b2)
julia> tryparse(SimpleColor, "#nocolor")Base.signed — Function
signed(x)Convert a number to a signed integer. If the argument is unsigned, it is reinterpreted as signed without checking for overflow.
See also: unsigned, sign, signbit.
signed(T::Integer)Convert an integer bitstype to the signed type of the same size.
Examples
julia> signed(UInt16)
Int16
julia> signed(UInt64)
Int64sourceBase.unsigned — Function
unsigned(T::Integer)Convert an integer bitstype to the unsigned type of the same size.
Examples
julia> unsigned(Int16)
UInt16
julia> unsigned(UInt64)
UInt64sourceBase.float — Method
Base.Math.significand — Function
significand(x)Extract the significand (a.k.a. mantissa) of a floating-point number. If x is a non-zero finite number, then the result will be a number of the same type and sign as x, and whose absolute value is on the interval $[1,2)$. Otherwise x is returned.
Examples
julia> significand(15.2)
1.9
julia> significand(-15.2)
-1.9
julia> significand(-15.2) * 2^3
-15.2
julia> significand(-Inf), significand(Inf), significand(NaN)
(-Inf, Inf, NaN)sourceBase.Math.exponent — Function
exponent(x::Real) -> IntReturn the largest integer y such that 2^y ≤ abs(x). For a normalized floating-point number x, this corresponds to the exponent of x.
Throws a DomainError when x is zero, infinite, or NaN. For any other non-subnormal floating-point number x, this corresponds to the exponent bits of x.
See also signbit, significand, frexp, issubnormal, log2, ldexp.
Examples
julia> exponent(8)
3
julia> exponent(6.5)
2
julia> exponent(-1//4)
-2
julia> exponent(3.142e-4)
-12
julia> exponent(floatmin(Float32)), exponent(nextfloat(0.0f0))
(-126, -149)
julia> exponent(0.0)
ERROR: DomainError with 0.0:
Cannot be ±0.0.
[...]sourceBase.complex — Method
complex(r, [i])Convert real numbers or arrays to complex. i defaults to zero.
Examples
julia> complex(7)
7 + 0im
julia> complex([1, 2, 3])
3-element Vector{Complex{Int64}}:
1 + 0im
2 + 0im
3 + 0imsourceBase.bswap — Function
bswap(n)Reverse the byte order of n.
(See also ntoh and hton to convert between the current native byte order and big-endian order.)
Examples
julia> a = bswap(0x10203040)
0x40302010
julia> bswap(a)
0x10203040
julia> string(1, base = 2)
"1"
julia> string(bswap(1), base = 2)
"100000000000000000000000000000000000000000000000000000000"sourceBase.hex2bytes — Function
hex2bytes(itr)Given an iterable itr of ASCII codes for a sequence of hexadecimal digits, returns a Vector{UInt8} of bytes corresponding to the binary representation: each successive pair of hexadecimal digits in itr gives the value of one byte in the return vector.
The length of itr must be even, and the returned array has half of the length of itr. See also hex2bytes! for an in-place version, and bytes2hex for the inverse.
Calling hex2bytes with iterators producing UInt8 values requires Julia 1.7 or later. In earlier versions, you can collect the iterator before calling hex2bytes.
Examples
julia> s = string(12345, base = 16)
"3039"
julia> hex2bytes(s)
2-element Vector{UInt8}:
0x30
0x39
julia> a = b"01abEF"
6-element Base.CodeUnits{UInt8, String}:
0x30
0x31
0x61
0x62
0x45
0x46
julia> hex2bytes(a)
3-element Vector{UInt8}:
0x01
0xab
0xefsourceBase.hex2bytes! — Function
hex2bytes!(dest::AbstractVector{UInt8}, itr)Convert an iterable itr of bytes representing a hexadecimal string to its binary representation, similar to hex2bytes except that the output is written in-place to dest. The length of dest must be half the length of itr.
Calling hex2bytes! with iterators producing UInt8 requires version 1.7. In earlier versions, you can collect the iterable before calling instead.
Base.bytes2hex — Function
bytes2hex(itr) -> String
bytes2hex(io::IO, itr)Convert an iterator itr of bytes to its hexadecimal string representation, either returning a String via bytes2hex(itr) or writing the string to an io stream via bytes2hex(io, itr). The hexadecimal characters are all lowercase.
Calling bytes2hex with arbitrary iterators producing UInt8 values requires Julia 1.7 or later. In earlier versions, you can collect the iterator before calling bytes2hex.
Examples
julia> a = string(12345, base = 16)
"3039"
julia> b = hex2bytes(a)
2-element Vector{UInt8}:
0x30
0x39
julia> bytes2hex(b)
"3039"sourceGeneral Number Functions and Constants
Base.one — Function
one(x)
one(T::Type)Return a multiplicative identity for x: a value such that one(x)*x == x*one(x) == x. If the multiplicative identity can be deduced from the type alone, then a type may be given as an argument to one (e.g. one(Int) will work because the multiplicative identity is the same for all instances of Int, but one(Matrix{Int}) is not defined because matrices of different shapes have different multiplicative identities.)
If possible, one(x) returns a value of the same type as x, and one(T) returns a value of type T. However, this may not be the case for types representing dimensionful quantities (e.g. time in days), since the multiplicative identity must be dimensionless. In that case, one(x) should return an identity value of the same precision (and shape, for matrices) as x.
If you want a quantity that is of the same type as x, or of type T, even if x is dimensionful, use oneunit instead.
See also the identity function, and I in LinearAlgebra for the identity matrix.
Examples
julia> one(3.7)
1.0
julia> one(Int)
1
julia> import Dates; one(Dates.Day(1))
1sourceBase.oneunit — Function
oneunit(x::T)
oneunit(T::Type)Return T(one(x)), where T is either the type of the argument, or the argument itself in cases where the oneunit can be deduced from the type alone. This differs from one for dimensionful quantities: one is dimensionless (a multiplicative identity) while oneunit is dimensionful (of the same type as x, or of type T).
Examples
julia> oneunit(3.7)
1.0
julia> import Dates; oneunit(Dates.Day)
1 daysourceBase.zero — Function
zero(x)
zero(::Type)Get the additive identity element for x. If the additive identity can be deduced from the type alone, then a type may be given as an argument to zero.
For example, zero(Int) will work because the additive identity is the same for all instances of Int, but zero(Vector{Int}) is not defined because vectors of different lengths have different additive identities.
See also iszero, one, oneunit, oftype.
Examples
julia> zero(1)
0
julia> zero(big"2.0")
0.0
julia> zero(rand(2,2))
2×2 Matrix{Float64}:
0.0 0.0
0.0 0.0sourceBase.MathConstants.pi — Constant
Base.MathConstants.ℯ — Constant
Base.MathConstants.catalan — Constant
catalanCatalan's constant.
Examples
julia> Base.MathConstants.catalan
catalan = 0.9159655941772...
julia> sum(log(x)/(1+x^2) for x in 1:0.01:10^6) * 0.01
0.9159466120554123sourceBase.MathConstants.eulergamma — Constant
γ
eulergammaEuler's constant.
Examples
julia> Base.MathConstants.eulergamma
γ = 0.5772156649015...
julia> dx = 10^-6;
julia> sum(-exp(-x) * log(x) for x in dx:dx:100) * dx
0.5772078382499133sourceBase.MathConstants.golden — Constant
φ
goldenThe golden ratio.
Examples
julia> Base.MathConstants.golden
φ = 1.6180339887498...
julia> (2ans - 1)^2 ≈ 5
truesourceBase.Inf32 — Constant
Base.Inf16 — Constant
Base.NaN32 — Constant
Base.NaN16 — Constant
Base.issubnormal — Function
Base.isfinite — Function
isfinite(f) -> BoolTest whether a number is finite.
Examples
julia> isfinite(5)
true
julia> isfinite(NaN32)
falsesourceBase.isone — Function
isone(x)Return true if x == one(x); if x is an array, this checks whether x is an identity matrix.
Examples
julia> isone(1.0)
true
julia> isone([1 0; 0 2])
false
julia> isone([1 0; 0 true])
truesourceBase.nextfloat — Function
nextfloat(x::AbstractFloat)Return the smallest floating point number y of the same type as x such that x < y. If no such y exists (e.g. if x is Inf or NaN), then return x.
See also: prevfloat, eps, issubnormal.
Base.prevfloat — Function
prevfloat(x::AbstractFloat)Return the largest floating point number y of the same type as x such that y < x. If no such y exists (e.g. if x is -Inf or NaN), then return x.
Base.isinteger — Function
isinteger(x) -> BoolTest whether x is numerically equal to some integer.
Examples
julia> isinteger(4.0)
truesourceBase.isreal — Function
isreal(x) -> BoolTest whether x or all its elements are numerically equal to some real number including infinities and NaNs. isreal(x) is true if isequal(x, real(x)) is true.
Examples
julia> isreal(5.)
true
julia> isreal(1 - 3im)
false
julia> isreal(Inf + 0im)
true
julia> isreal([4.; complex(0,1)])
falsesourceCore.Float32 — Method
Float32(x [, mode::RoundingMode])Create a Float32 from x. If x is not exactly representable then mode determines how x is rounded.
Examples
julia> Float32(1/3, RoundDown)
0.3333333f0
julia> Float32(1/3, RoundUp)
0.33333334f0See RoundingMode for available rounding modes.
Core.Float64 — Method
Float64(x [, mode::RoundingMode])Create a Float64 from x. If x is not exactly representable then mode determines how x is rounded.
Examples
julia> Float64(pi, RoundDown)
3.141592653589793
julia> Float64(pi, RoundUp)
3.1415926535897936See RoundingMode for available rounding modes.
Base.Rounding.rounding — Function
Base.Rounding.setrounding — Method
setrounding(T, mode)Set the rounding mode of floating point type T, controlling the rounding of basic arithmetic functions (+, -, *, / and sqrt) and type conversion. Other numerical functions may give incorrect or invalid values when using rounding modes other than the default RoundNearest.
Note that this is currently only supported for T == BigFloat.
This function is not thread-safe. It will affect code running on all threads, but its behavior is undefined if called concurrently with computations that use the setting.
Base.Rounding.setrounding — Method
setrounding(f::Function, T, mode)Change the rounding mode of floating point type T for the duration of f. It is logically equivalent to:
old = rounding(T)
setrounding(T, mode)
f()
setrounding(T, old)See RoundingMode for available rounding modes.
Base.Rounding.get_zero_subnormals — Function
get_zero_subnormals() -> BoolReturn false if operations on subnormal floating-point values ("denormals") obey rules for IEEE arithmetic, and true if they might be converted to zeros.
Base.Rounding.set_zero_subnormals — Function
set_zero_subnormals(yes::Bool) -> BoolIf yes is false, subsequent floating-point operations follow rules for IEEE arithmetic on subnormal values ("denormals"). Otherwise, floating-point operations are permitted (but not required) to convert subnormal inputs or outputs to zero. Returns true unless yes==true but the hardware does not support zeroing of subnormal numbers.
set_zero_subnormals(true) can speed up some computations on some hardware. However, it can break identities such as (x-y==0) == (x==y).
Integers
Base.count_ones — Function
count_ones(x::Integer) -> IntegerNumber of ones in the binary representation of x.
Examples
julia> count_ones(7)
3
julia> count_ones(Int32(-1))
32sourceBase.count_zeros — Function
count_zeros(x::Integer) -> IntegerNumber of zeros in the binary representation of x.
Examples
julia> count_zeros(Int32(2 ^ 16 - 1))
16
julia> count_zeros(-1)
0sourceBase.leading_zeros — Function
leading_zeros(x::Integer) -> IntegerNumber of zeros leading the binary representation of x.
Examples
julia> leading_zeros(Int32(1))
31sourceBase.leading_ones — Function
leading_ones(x::Integer) -> IntegerNumber of ones leading the binary representation of x.
Examples
julia> leading_ones(UInt32(2 ^ 32 - 2))
31sourceBase.trailing_zeros — Function
trailing_zeros(x::Integer) -> IntegerNumber of zeros trailing the binary representation of x.
Examples
julia> trailing_zeros(2)
1sourceBase.trailing_ones — Function
trailing_ones(x::Integer) -> IntegerNumber of ones trailing the binary representation of x.
Examples
julia> trailing_ones(3)
2sourceBase.isodd — Function
isodd(x::Number) -> BoolReturn true if x is an odd integer (that is, an integer not divisible by 2), and false otherwise.
Examples
julia> isodd(9)
true
julia> isodd(10)
falsesourceBase.iseven — Function
iseven(x::Number) -> BoolReturn true if x is an even integer (that is, an integer divisible by 2), and false otherwise.
Examples
julia> iseven(9)
false
julia> iseven(10)
truesourceCore.@int128_str — Macro
Core.@uint128_str — Macro
BigFloats and BigInts
The BigFloat and BigInt types implements arbitrary-precision floating point and integer arithmetic, respectively. For BigFloat the GNU MPFR library is used, and for BigInt the [GNU Multiple Precision Arithmetic Library (GMP)] (https://gmplib.org) is used.
Base.MPFR.BigFloat — Method
BigFloat(x::Union{Real, AbstractString} [, rounding::RoundingMode=rounding(BigFloat)]; [precision::Integer=precision(BigFloat)])Create an arbitrary precision floating point number from x, with precision precision. The rounding argument specifies the direction in which the result should be rounded if the conversion cannot be done exactly. If not provided, these are set by the current global values.
BigFloat(x::Real) is the same as convert(BigFloat,x), except if x itself is already BigFloat, in which case it will return a value with the precision set to the current global precision; convert will always return x.
BigFloat(x::AbstractString) is identical to parse. This is provided for convenience since decimal literals are converted to Float64 when parsed, so BigFloat(2.1) may not yield what you expect.
See also:
precision as a keyword argument requires at least Julia 1.1. In Julia 1.0 precision is the second positional argument (BigFloat(x, precision)).
Examples
julia> BigFloat(2.1) # 2.1 here is a Float64
2.100000000000000088817841970012523233890533447265625
julia> BigFloat("2.1") # the closest BigFloat to 2.1
2.099999999999999999999999999999999999999999999999999999999999999999999999999986
julia> BigFloat("2.1", RoundUp)
2.100000000000000000000000000000000000000000000000000000000000000000000000000021
julia> BigFloat("2.1", RoundUp, precision=128)
2.100000000000000000000000000000000000007sourceBase.precision — Function
precision(num::AbstractFloat; base::Integer=2)
precision(T::Type; base::Integer=2)Get the precision of a floating point number, as defined by the effective number of bits in the significand, or the precision of a floating-point type T (its current default, if T is a variable-precision type like BigFloat).
If base is specified, then it returns the maximum corresponding number of significand digits in that base.
Base.MPFR.setprecision — Function
setprecision(f::Function, [T=BigFloat,] precision::Integer; base=2)Change the T arithmetic precision (in the given base) for the duration of f. It is logically equivalent to:
old = precision(BigFloat)
setprecision(BigFloat, precision)
f()
setprecision(BigFloat, old)Often used as setprecision(T, precision) do ... end
Note: nextfloat(), prevfloat() do not use the precision mentioned by setprecision.
There is a fallback implementation of this method that calls precision and setprecision, but it should no longer be relied on. Instead, you should define the 3-argument form directly in a way that uses ScopedValue, or recommend that callers use ScopedValue and @with themselves.
setprecision([T=BigFloat,] precision::Int; base=2)Set the precision (in bits, by default) to be used for T arithmetic. If base is specified, then the precision is the minimum required to give at least precision digits in the given base.
This function is not thread-safe. It will affect code running on all threads, but its behavior is undefined if called concurrently with computations that use the setting.
Base.GMP.BigInt — Method
BigInt(x)Create an arbitrary precision integer. x may be an Int (or anything that can be converted to an Int). The usual mathematical operators are defined for this type, and results are promoted to a BigInt.
Instances can be constructed from strings via parse, or using the big string literal.
Examples
julia> parse(BigInt, "42")
42
julia> big"313"
313
julia> BigInt(10)^19
10000000000000000000sourceCore.@big_str — Macro
@big_str strParse a string into a BigInt or BigFloat, and throw an ArgumentError if the string is not a valid number. For integers _ is allowed in the string as a separator.
Examples
julia> big"123_456"
123456
julia> big"7891.5"
7891.5
julia> big"_"
ERROR: ArgumentError: invalid number format _ for BigInt or BigFloat
[...]Using @big_str for constructing BigFloat values may not result in the behavior that might be naively expected: as a macro, @big_str obeys the global precision (setprecision) and rounding mode (setrounding) settings as they are at load time. Thus, a function like () -> precision(big"0.3") returns a constant whose value depends on the value of the precision at the point when the function is defined, not at the precision at the time when the function is called.